Every core Azure data and analytics service on one page — what it does, when to use it, and how they compare. A quick reference for architects building modern data platforms.
| Service | Category | Best For | Query Language | Key Differentiator |
|---|---|---|---|---|
| Synapse Analytics | Warehouse + Spark | Unified DWH & big data | T-SQL / Spark | Serverless + dedicated pools |
| Microsoft Fabric | SaaS analytics | End-to-end data platform | T-SQL / KQL / DAX | OneLake, unified SaaS |
| Data Factory | ETL / Orchestration | Data integration pipelines | Visual / Code | 100+ connectors |
| Databricks | Lakehouse / ML | Data engineering & science | Spark SQL / Python | Delta Lake, Unity Catalog |
| ADLS Gen2 | Storage | Analytics data lake | N/A (storage) | Hierarchical namespace |
| Stream Analytics | Streaming | Real-time event processing | Stream SQL | No-code streaming |
| Event Hubs | Ingestion | High-throughput event intake | N/A (ingest) | Kafka compatible |
| HDInsight | Open-source | Hadoop/Spark clusters | HiveQL / Spark SQL | Multi-framework managed |
| Data Explorer | Real-time analytics | Telemetry & log analysis | KQL | Sub-second queries |
| Power BI | BI / Visualization | Dashboards & reporting | DAX / M | Self-service enterprise BI |